Additive regression model ($\mathbb{E}(Y\mid x)={\beta }_0+{\sum }_j{f}_j(x_j)$)

An additive regression model represents a conditional mean as a sum of univariate functions and an intercept. It permits nonlinear effects without estimating a general high-dimensional response surface. The backfitting algorithm smooths each component's partial residual. Centering separates constants from the intercept, while concurvity can still prevent component identification.

// Target: probability-and-statistics.bigb

In an additive regression model, each component can transfer a constant to the regression intercept, so impose empirical mean-zero constraints, or population mean-zero constraints when appropriate. Even after centering, nonzero component perturbations $h_j$ with ${\sum }_j{h}_j(x_{ij})=0$ at every observed point make the additive decomposition nonidentifiable. Extra smoothing penalties may resolve the fitted decomposition, but centering alone does not remove concurvity.

// Target: probability-and-statistics.bigb

Concurvity is the nonlinear analogue of multicollinearity: a function of one predictor is exactly or approximately expressible as a sum of functions of other predictors. Exact concurvity can make an additive decomposition nonunique; approximate concurvity makes components difficult to estimate stably. A unique fitted sum need not imply unique individual components.

// Target: probability-and-statistics.bigb